function Average_Precision=Average_precision(Outputs,test_target)
%Computing the average precision
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class is stored in Outputs(j,i)
%test_target: the actual labels of the test instances, if the ith instance belong to the jth class, test_target(j,i)=1, otherwise test_target(j,i)=-1

[num_class,num_instance]=size(Outputs);
temp_Outputs=[];
temp_test_target=[];
for i=1:num_instance
    temp=test_target(:,i);
    if((sum(temp)~=num_class)&&(sum(temp)~=-num_class))
        temp_Outputs=[temp_Outputs,Outputs(:,i)];
        temp_test_target=[temp_test_target,temp];
    end
end
Outputs=temp_Outputs;
test_target=temp_test_target;
[num_class,num_instance]=size(Outputs);

Label=cell(num_instance,1);
not_Label=cell(num_instance,1);
Label_size=zeros(1,num_instance);
for i=1:num_instance
    temp=test_target(:,i);
    Label_size(1,i)=sum(temp==ones(num_class,1));
    for j=1:num_class
        if(temp(j)==1)
            Label{i,1}=[Label{i,1},j];
        else
            not_Label{i,1}=[not_Label{i,1},j];
        end
    end
end

aveprec=0;
for i=1:num_instance
    temp=Outputs(:,i);
    [~,index]=sort(temp);
    indicator=zeros(1,num_class);
    for m=1:Label_size(i)
        [~,loc]=ismember(Label{i,1}(m),index);
        indicator(1,loc)=1;
    end
    summary=0;
    for m=1:Label_size(i)
        [~,loc]=ismember(Label{i,1}(m),index);
        summary=summary+sum(indicator(loc:num_class))/(num_class-loc+1);
    end
    %         ap_binary(i)=summary/Label_size(i);
    aveprec=aveprec+summary/Label_size(i);
end
Average_Precision=aveprec/num_instance;